Visual Inspection of Precision Instruments by Least-Squares Outlier Detection

抄録

Visual inspection is one of the most important processes in precision instrument factories for screening out products of poor quality. Usually this is carried out by human experts who went through long-term training sessions, so there is a strong demand for automating this process in order to reduce production costs. In this paper, we apply a recently-developed outlier detection method called least-squares outlier detection (LSOD) to this task and demonstrate that inferior products can be successfully detected. LSOD can utilize knowledge of inliers for enhancing outlier detection performance, so it suits well to visual inspection in industries. Furthermore, LSOD is equipped with automatic model selection mechanism and, hence, users do not have to grapple with parameter tuning.